A Lab-based Test To Evaluate Pedometer Longevity

IntroductionChronic respiratory disease (CRD) is associated with reduced physical activity (PA), which pulmonary rehabilitation (PR) can improve. There are a variety of devices which monitor PA. Activity monitors (AM) are...

Objective: To assess (1) step count accuracy of the Fitbit Zip, compared to manual step count, in people receiving outpatient rehabilitation, in indoor and outdoor conditions, and (2) impact of slow walking speed on Fitbit accuracy. Design: Observational study. Setting: A metropolitan rehabilitation hospital. Subjects: Adults (n = 88) attending a subacute rehabilitation outpatient clinic with walking speeds of between 0.4 and 1.0 m/s. Interventions: Two 2-minute walk tests, one indoors and one outdoors, completed in random order. Main measures: Step count recorded manually by observation and by a Fitbit Zip, attached to the shoe on the dominant or non-affected side. Subgroup analysis included assessment accuracy for those considered limited community walkers (slower than 0.8 m/s) and those considered community walkers (faster than 0.8 m/s). Results: The Fitbit significantly (P < 0.05) undercounted steps compared to manual step count, indoors and outdoors, with percentage agreement slightly higher outdoors (mean 92.4%) than indoors (90.1%). Overall, there was excellent consistent agreement between the Fitbit and manual step count for both indoor (ICC 0.83) and outdoor (ICC 0.88) walks. The accuracy of the Fitbit was significantly (P < 0.05) reduced in those who walked slower than 0.8 m/s outdoors (ICC 0.80) compared to those who walk faster than 0.8 m/s (ICC 0.90). Conclusions: The Fitbit Zip shows high step count accuracy with manual step count in a mixed subacute rehabilitation population. However, accuracy is affected by walking speed, with decreased accuracy in limited community walkers.

Accuracy of step count measured by physical activity monitors: The effect of gait speed and anatomical placement site

Objective: To examine the step count accuracy of activity monitors in community-dwelling older adults. Method: Twenty-nine participants aged 67.70 ± 6.07 participated. Three pedometers and the Actical accelerometer step count functions were compared with actual steps taken during a 200-m walk around an indoor track and during treadmill walking at three different speeds. Results: There was no statistical difference between activity monitors step counts and actual steps during self-selected pace walking. During treadmill walking at 0.67 m∙s−1, all activity monitors step counts were significantly different from actual steps. During treadmill walking at 0.894m∙s−1, the Omron HJ-112 pedometer step counts were not significantly different from actual steps. During treadmill walking at 1.12 m∙s−1, the Yamax SW-200 pedometer steps were significantly different from actual steps. Discussion: Activity monitor selection should be deliberate when examining the walking behaviors of community-dwelling older adults, especially for those who walk at a slower pace.

To compare (i) the convergent validity of the self-report Zutphen Physical Activity Questionnaire with the 7-d objective physical activity (PA) measurement by accelerometers and pedometers and (ii) the construct validity of these measures by examining their associations with physical health and psychological and anthropometric variables. Five hundred and sixty community-dwelling people aged > or =65 yr were invited from a UK primary care practice and 238 (43%) participated (mean age = 74, 53% male). PA was assessed subjectively by the Zutphen questionnaire (modified to include housework questions) and objectively by the 7-d accelerometer monitoring: a random half also had a pedometer. A questionnaire assessed health, disability, and psychological factors, and anthropometric assessment was performed. Mean daily PA levels were as follows: Zutphen = 9.1 kcal x kg(-1) x d(-1) (SD = 6.6 kcal x kg(-1) x d(-1)); accelerometer activity count = 226,648 (SD = 121,966); accelerometer step count = 6495 (SD = 3212); and pedometer step count = 6712 (SD = 3526). Zutphen score was moderately correlated with accelerometer activity count (R = 0.34, P < 0.001) and pedometer step count (R = 0.36, P < 0.001). Pedometer step count was highly correlated with accelerometer activity count (R = 0.82, P< 0.001) and accelerometer step count (R = 0.86, P < 0.001). Objective PA measures showed strong associations with health and anthropometric and psychological variables. Zutphen score was not significantly related to most health or anthropometric measures but was associated with psychological variables and provided information about activity type. Convergent validity was strong between accelerometers and pedometers but weaker between these and self-report Zutphen. Pedometers may be preferred to accelerometers for simple studies due to their lower cost. Objective measures had better construct validity, being more strongly associated with established PA determinants, and thus offered better value to researchers than the questionnaire, but the latter provided useful detail on activity type, so a combined approach to PA assessment may be preferable.

BackgroundThe European pedometer-based "10,000 Steps Ghent" whole community intervention for 228,000 residents was found to be effective in increasing step counts by an average of 896 steps/day in a sub-sample of adults. The present study aimed to examine the characteristics of intervention participants (n = 438) who (1) used a pedometer and (2) increased their step counts. Additionally, the third aim was to examine the mediational effect of pedometer use on step count change.MethodsThe study sample consisted of 438 adults (207 male, mean age 49.8 (13.1) years). Binary logistic regressions were used to examine whether individual characteristics (gender, age, educational level, employment status, self-reported health condition, baseline step counts, baseline sitting time, baseline transport-related PA) and intervention exposure variables (having heard/seen a PA promotion message, being aware of the PA guidelines, and knowing about "10,000 Steps Ghent") were associated with (1) pedometer use and (2) a step count increase of 896 steps/day or more. Using pooled data (n = 864) from the intervention and comparison participants, a mediation analysis was conducted to see if the change in step counts was mediated by pedometer use.ResultsAge (49 years or more: OR = 3.19, p < 0.005), awareness of a PA promotion message (OR = 2.62, p < 0.01) and awareness of "10,000 Steps Ghent" (OR = 2.11, p < 0.05) were significantly associated with pedometer use. Participants with a college or university degree (OR = 1.55, p < 0.05) and those who used a pedometer (OR = 2.06, p < 0.05) were more likely to increase their steps by 896 steps/day or more. This increase was less likely among those with baseline step counts above 10,000 steps/day (OR = 0.38, p < 0.001). The mediation analysis revealed that pedometer use partly mediated step count change.ConclusionPedometer use was more likely in older participants and in those who were aware of the "10,000 Steps" campaign. Increasing step counts was more likely among those with higher education, baseline step counts below 10,000 steps/day and those who used a pedometer. Pedometer use only partly mediated the intervention effect on step counts.

BackgroundCommercially available activity monitors, such as the Fitbit, may encourage physical activity. However, the accuracy of the Fitbit in older adults remains unknown. This study aimed to determine (1) the...

A Smartphone App Shows Patients Return to Preoperative Gait Metrics 6 Weeks After Hip Arthroscopy, and Gait Metrics Have Low to Moderate Correlations With a Hip-Specific Patient-Reported Outcome Measure

Background and study aim Video capsule endoscopy (VCE) is limited by reliance on bowel motility for propulsion, and lack of physical activity has been proposed as a cause of incomplete studies. Our aim was to prospectively investigate the association between physical activity and VCE bowel transit.Patients and methods Ambulatory outpatients receiving VCE were eligible for the study. A pedometer was attached at the time of VCE ingestion and step count was recorded at the end of the procedure. VCE completion was assessed by logistic regression models, which included step count (500 steps as one unit). Total transit time was analyzed by Cox proportional hazards models. The hazard ratios (HR) with 95 % confidence interval (CI) indicated the “hazard” of completion, such that HRs > 1 indicated a reduced transit time.Results A total of 100 patients were included. VCE was completed in 93 patients (93 %). The median step count was 2782 steps. Step count was not significantly associated with VCE completion (odds ratio 1.45, 95 %CI 0.84, 2.49). Pedometer step count was significantly associated with shorter total, gastric, and small-bowel transit times (HR 1.09, 95 %CI 1.03, 1.16; HR 1.05, 95 %CI 1.00, 1.11; HR 1.07, 95 %CI 1.01, 1.14, respectively). Higher body mass index (BMI) was significantly associated with VCE completion (HR 1.87, 95 %CI 1.18, 2.97) and shorter bowel transit times (HR 1.05, 95 %CI 1.02, 1.08).Conclusions Increased physical activity during outpatient VCE was associated with shorter bowel transit times but not with study completion. In addition, BMI was a previously unreported clinical characteristic associated with VCE completion and should be included as a variable of interest in future studies.

Introduction: Video capsule endoscopy (VCE) is the preferred modality for assessment of the small bowel mucosa, but it is limited by the battery lifespan of the device and reliance on bowel motility for propulsion. Lack of physical activity has been proposed as a potential cause of incomplete studies. The aim of this study was to prospectively investigate the association between physical activity and VCE completion and bowel transit times. Methods: All ambulatory outpatients undergoing VCE were assessed for inclusion. After consent, relevant clinical characteristics were obtained. At the time of ingestion of a VCE with standard 8 hour battery, a sealed pedometer was attached to the patient. At the conclusion of the procedure, the step-count was recorded. VCE completion was assessed by univariable and multivariable logistic regression models which included pedometer step count (500 steps as one unit). Total transit time was analyzed by univariable and multivariable Cox proportional hazards models. Gastric and small bowel transit times were assessed in univariable Cox proportional hazards models. The hazard ratios (HR) indicate the “hazard” of completion, such that hazard ratios greater than 1 indicate reduced transit time. Results: One hundred patients were included, with mean age 50.9 years and 64% female. VCE was completed in 93 patients (93%). The mean step count was 3,125 steps (SD = 2,070). Pedometer step count was significantly associated with shorter total transit times (HR = 1.08; 95% CI 1.02, 1.14), gastric transit times (HR = 1.06; 95% CI 1.01, 1.11) and small bowel transit times (HR = 1.06; 1.01, 1.12) in univariable analysis. Step counts remained significantly associated with total transit times in the multivariable model (HR = 1.09; 95% CI 1.03, 1.16; Table 1). In univariable and multivariable analysis, step count was not significantly associated with VCE completion (p = 0.17 and 0.15 respectively; Table 2).Table 1: Multivariable Cox proportional hazards model for video capsule endoscopy total transit timeTable 2: Multivariable logistic regression for video capsule endoscopy completionConclusion: In the first study to use a validated tool to directly measure physical activity, higher step counts during outpatient VCE correlated with shorter bowel transit times but were not significantly associated with study completion. We suspect the decrease in bowel transit times did not translate into higher rates of study completion due to the high baseline completion rate. Given the lack of negative side effects, we recommend encouraging physical activity during VCE studies.

Wearable activity devices, widely used to monitor physical activity in non-injured populations, have shown potential in encouraging early ambulation and enhanced recovery in hospitalised patients. This study evaluated the accuracy of wearable devices in tracking step counts under simulated hospital conditions, seeking the optimal body location placement for individuals with altered gait. This method comparison study involved healthy adults walking on a treadmill while performing slow and shuffling walking patterns. Twelve wearable devices were placed on the arm, waist, and leg, and their recorded step counts were compared to manual counts from filmed sessions, following Consumer Technology Association guidelines. The Apple Watch, particularly when worn on the waist, demonstrated the highest reliability and adaptability across walking patterns. Leg placement, which accounted for 10 of the top 20 device-position combinations, suggested that larger movement amplitudes improve step count accuracy, particularly during slow or altered gaits. This study confirmed the Apple Watch to be the most accurate wearable step count device. The study provides new understanding as to the precision of commercially available devices and their placement, when aiming to improve and, or conduct research about, patient physical activity outcomes.

Consumer Technology Association guidelines recommend two independent researchers review video recordings of treadmill bouts to verify accuracy of the number of steps taken. Performing multiple post hoc validation checks requires significant resources for personnel time and costs, but it is unknown if the burden is necessary. The purpose of this study was to evaluate accuracy and efficiency of step counts assessed during direct observation compared with multiple reviews of video recordings. Participants ages 18-20 years (n = 72) completed up to twelve 5-min, zero-incline treadmill bouts, speeds ranging 0.2-2.7 m/s (0.5-6.0 mph). Directly observed steps were counted and hand-tallied in real-time. A sex-age stratified random sample of 30 participants' bouts were selected for analysis. Two independent researchers recounted steps from video recordings, producing three independent counts. Intraclass correlation coefficient determined interrater reliability. Mean average percent deviation computed deviation magnitude among the three counts. Correlation tests determined the relationship between absolute count differences and average counts for each pairwise comparison. Interrater reliability was excellent with intraclass correlation coefficients ≥ .995 (p < .001) across all speeds. Average mean average percent deviation was 0.29% with bout-specific deviations ranging from 0.10% to 0.54%, indicating < 1% difference among the three counts for all bouts. A single real-time step count represents a sufficient and efficient approach to measure steps taken in a controlled treadmill setting, while video recounts serve as back-up to allay potential data loss concerns and/or for random selection verification. These data-driven findings are useful for optimizing personnel time and reducing costs associated with collecting quality data in large-scale treadmill-based step-counting studies.

ObjectiveTo determine whether the walking speed maintained during a 1 km treadmill test at moderate intensity predicts survival in patients with cardiovascular disease.DesignPopulation-based prospective study.SettingOutpatient secondary prevention programme in Ferrara,...

This study aimed to investigate the accuracy and reliability of accelerometer-based pedometers placed on the wrist, waist, and shoe’s midsole during walking, running, and stair climbing. Twenty healthy adults were recruited. Steps were recorded by the pedometers and visually assessed from simultaneously recorded video to evaluate the accuracy of each pedometer in different locations of attachment. One week later, steps were recorded again by the pedometers only to evaluate the reliability of each pedometer in different locations of attachment. The wrist-worn pedometer presented significantly greater error scores compared to the midsole-worn pedometer during walking (p < 0.001), running (p = 0.006), and stair climbing (p = 0.003). Additionally, mean absolute precent error and Bland-Altman plots indicated that the pedometer worn in the midsole was most accurate for running and stair climbing, followed by waist-worn and wrist-worn pedometers. Furthermore, the midsole-worn and waist-worn pedometers showed strong reliability during walking and running, but only the midsole-worn pedometer presented acceptable reliability during stair climbing. The pedometer’s position impacts the accuracy and reliability of step counts, especially for walking and stair climbing. Embedding the pedometer into the midsole seems an effective approach to improve the accuracy and reliability of step counts.

IntroductionWalking speed has been associated with many clinical outcomes (e.g., frailty, mortality, joint replacement need, etc.). Accurately measuring walking speed (stride length x step count/time) typically requires significant clinician/staff time or a gait lab with specialized equipment (i.e., electronic timers or motion capture). In the present study, our goal was to measure “step count” via smartphones through novel software and to compare with step tracking software that come standard with iOS and Android smartphones as a first step in walking speed measurement.MethodsA separate calibration and validation data collection was performed. Individuals in the calibration collection (n = 5) walked 20m at normal and slow speed (<1.0 m/s). Appropriate settings for the novel mobile application were chosen to measure step count. Individuals in the validation (n = 52) collection walked at 6m, 10m, and 20m at normal and slow walking speeds. We compared step difference (absolute difference) from observed step counts to native step tracking software and our novel software derived step counts. We used generalized estimated equation adjusted (participant level) negative binomial regression models of absolute step difference from observed step counts, to determine optimal settings (calibration) and subsequently to gauge performance of the shake algorithm settings and native step tracking software across different distances and speeds (validation).ResultsFor iOS/iPhone 6, when compared to observed step count, the shake service (software driven approach) significantly outperformed the embedded native step tracking software across all distances at slow speed, and for short distance (6m) at normal speed. On the Android phone, the shake service outperformed the native step tracking software at slow speed at 6 meters and 20 meters, while its performance eclipsed the native step tracking software only at 20 meters at normal speed.DiscussionOur software based approach outperformed native step tracking software across various speeds and distances and carries the advantage of having adjustable measurement parameters that can be further optimized for specific medical conditions. Such software applications will provide an effective way to capture standardized data across multiple commercial smartphone devices, facilitating the future capture of walking speed and other clinically important performance parameters that will influence clinical and home care in the era of value based care.